In an evolving environment of increased competition, changing customer behaviour and the need for tight risk management, financial institutions are deploying business intelligence (BI) solutions to more accurately target clients and improve risk mitigation. Such solutions deliver relevant, actionable and timely information that enable quick, data-driven decisions to be made.

Jonathon Traer-Clark, head of strategy and advisory for global transaction services, Bank of America Merrill Lynch, says his institution is seeking a 360-degree view of each client. “We are a client-centric business. From a marketing perspective, we use analytics to target specific initiatives, products or solutions for clients. BI supports all the decisions that we make – including those related to risk identification and mitigation – and allows us to drive better business outcomes with our clients.”

Getting the right information to the right people so they can make appropriate decisions is a focus at Barclays, says Daniel Subramaniam, vice-president of data analytics. “Often data repositories are quite similar but are used by multiple people in different ways. For example, executives at the C-suite level are looking to drive broad strategic decisions; therefore they understand that data in a different way from the operational leads on the ground. The question for us is how to deploy data to a very specific audience, without compromising its quality.”

The relevance and timeliness of data is important in an industry that encompasses many different functions, agrees Duncan Ash, senior director, global financial services at data analytics software company, Qlik. “Banks must carefully consider how they slice up the data. The information that someone in a group function in head office needs is different from another person who works in a branch. It can’t be too complicated – you need to always think about how you simplify that information and deliver it to the right person.”

Deploying BI solutions, which includes aggregating and analysing data across business units in financial institutions’ complex information IT infrastructures, is challenging. Xavier Gonzalez Farran, big data analytical tools director, CaixaBank, says: “In addition to the usual technology challenges such as infrastructure and data architecture, there is also an important challenge related to cultural change management. BI requires new roles, new committees, new procedures; this is very complex when you have a company with thousands of employees.”

Banks should consider carefully how BI solutions will be used and what the desired outcome is in terms of the timing and accuracy of the data sets, he adds. In structuring an information landscape, context is important. “The lens through which someone views information is very relevant to how we construct information.”

Turning big data into intelligence

Big data has always existed, argues Mr Subramaniam, but what is new is banks’ ability to obtain insights from the vast amounts of structured and unstructured data. But this is not easy, as Mr Traer-Clark points out, “One of my team members likened big data to finding a needle in a haystack – big data is the haystack and the needle is the intelligence we want to extract from it.”

Another challenge related to big data is the question around who owns the data. When CaixaBank sought to break down its silos of information and create a single corporate data model, Mr Farran explains the change in emphasis from who owns the data to who looks after it. When CaixaBank began its BI project around 18 months ago, it formed a data governance committee comprised of an executive director from each of three departments: financial, risk and IT. “As part of the new governance model, the committee defined all the data and how it is transformed. They also formalised information about the data’s origin, enabling the bank to meet regulatory requirements,” he says.

With regulations such as the Payment Services Directive 2 (PSD2) enabling greater data sharing, who owns data may no longer be relevant, adds Mr Subramaniam. “As more information is expected to be shared, the issue becomes who the custodian of the data is,” he says, agreeing with Mr Farran. “The more important consideration is what banks do with the data and how they derive value from it. Data governance committees must work with all parts of the bank to determine which information can be disseminated to which groups and how that information can be used.”

By bringing together different departments and breaking down data silos, banks can move from data management to BI, effectively extracting value from their data. “I think when information is shared and analytical techniques are applied to that combined data, genuine insights can be made that will give banks competitive advantage,” says Mr Ash.

Defining and implementing an enterprise data strategy once silos have been dismantled is essential in establishing controls around data and understanding how data elements relate to each other. According to Mr Ash, clearly defined data strategies require relationships to be forged between different areas of a bank that may not have communicated previously. “You have to enable parts of the business to get together and share sources of information to solve specific problems, sell a product or manage a certain type of risk. Silos are gone; working together is the new normal.”

Data governance and security

In breaking down silos, however, Mr Traer-Clark warns that banks must respect their regulatory obligations and ensure that businesses operate correctly. “Bank of America has a comprehensive data governance framework that is based on ‘need to know’. Someone has to have a very good rationale for wanting to look at a particular piece of information; they can’t just browse the data.”

Global banks must be cognisant of the varied data related regulations across the world and the limitations they place on banks, he adds. “In certain jurisdictions, the way you look at data is very strictly controlled, both from policy and enforcement perspectives. That’s where technology can be of assistance.”

The EU’s General Data Protection Regulation, which aims to strengthen and unify data protection for individuals, is a challenge for banks, says Mr Farran. “One of the concerns is how we get the explicit consent of our 14 million customers to integrate their data into the corporate model. This will be a big challenge in the coming months.”

Mr Subramaniam believes the need to gain opt-in from customers “really changes the dynamic of the relationship”, making it much more active as customers agree to the bank using their data or delivering information via application programming interfaces (APIs). “Organisations will need to write a whole new set of processes because this is uncharted territory. But I think it will create a much stronger trust relationship between banks and customers.” Mr Traer-Clark agrees, pointing out that by opting in, customers are asking banks to be the digital custodian of their information assets.

The future of BI and data analytics

The belief that data management and governance is the preserve of a specific department looks likely to diminish as advances in digital technology herald an age of ‘data democratisation’. Put simply, this means that information in a digital format is accessible to the average end user; non-specialists will be able to gather and analyse data without requiring help.

Mr Farran believes data democratisation will help banks to become more efficient. “CaixaBank has hundreds of people at headquarters whose day job is preparing information for other people in the bank. In promoting self-service data discovery and analytics we believe we can optimise our data management.”

For Mr Traer-Clark, data dissemination across the organisation is “only barely touching the surface of what is possible”. Bank of America Merrill Lynch tries to think about BI “openly and boldly”, he says, to determine exactly what it will mean for the bank and its customers.

For self-service data to work, the basics must be right, according to Qlik’s Mr Ash. “Data governance, security and management have to be better than ever if advanced technologies, such as artificial intelligence, are going to act upon that data. We need to be very clear about what data can be used by machine learning and robots, which will all have a place in BI. There are huge opportunities, but initially we must build platforms that will support analytics, self-service and advanced technologies.”

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